Method and data processing system for providing a stroke information

ABSTRACT

One or more example embodiments of the invention relates in one aspect to a computer-implemented method for providing a stroke information. The method includes receiving examination data, the examination data comprising computed tomography imaging data of an examination area of a patient, the examination area of the patient comprising at least one brain region, the at least one brain region being affected by a stroke; adjusting a causal model based on the computed tomography imaging data to obtain an adjusted causal model, wherein the adjusted causal model models a first variable as a first cause for an appearance of the examination area of the patient; receiving a first value for the first variable; generating the stroke information based on the adjusted causal model and the first value for the first variable; and providing the stroke information.

CROSS-REFERENCE TO RELATED APPLICATION(S)

The present application claims priority under 35 U.S.C. § 119 toEuropean Patent Application No. 22166025.1, filed Mar. 31, 2022, theentire contents of which are incorporated herein by reference.

FIELD

One or more example embodiments of the invention relates to acomputer-implemented method for providing a stroke information. In otheraspects, one or more example embodiments of the invention relates to adata processing system, to a computed tomography device, to a computerprogram product and to a computer-readable storage medium.

RELATED ART

When diagnosing patients suffering from acute ischemic stroke, the onsettime is clinically crucial information determining the most promisingtreatment choice. This is because longer times-to-treatment aregenerally associated with worse outcomes, thus reducing the expectedrisk-to-benefit ratio of common therapeutic measures. Unfortunately, theonset time is not always known and may need to be estimated. As oneoption, this may be done based on, e.g., computed tomography (CT) ormagnetic resonance (MR) imaging, and thus also constitutes a taskpotentially suitable for automation using learning-based methods. Atypical learning-based regression model could yield an estimated timeand may provide confidence scores and/or saliency/attention mapshighlighting decision-relevant regions in the input. Typically nofurther explanation is available, in-depth investigation is unfeasibleand there is little to provide clinical intuition about the result forthe end user.

In a similar fashion, considering the opposite temporal direction, itwould be useful to obtain a prediction of the expected short-termprogression given the currently available information—again in a waythat is easily recognized by a radiologist or clinician.

Manual or semi-automatic estimation of onset time based on quantitativemeasurements performed within the images is known and can provide somemeasure of explanation based on the physical models behind thecalculation. E.g., a water uptake can be estimated based on a densityloss derived from reduced HU values. The water uptake can in turn belinked to the onset time by the average speed of water uptake as aresult of infarction.

SUMMARY

However, these methods are limited to those specific static models anddo not provide any further visual intuition.

One or more example embodiments facilitate an assessment of stroke thatis improved with regard to intuitive exploration and validity checkingby a clinical user. This subject matter is set forth in at least theindependent claims and the dependent claims.

BRIEF DESCRIPTION OF THE DRAWINGS

Reference is made to the fact that the described methods and thedescribed systems are merely preferred example embodiments of theinvention, and that the invention can be varied by a person skilled inthe art, without departing from the scope of the invention as it isspecified by the claims.

The invention will be illustrated below with reference to theaccompanying figures using example embodiments. The illustration in thefigures is schematic and highly simplified and not necessarily to scale.

FIG. 1 shows an exemplary template for causal graphs representing anadjusted causal model.

FIG. 2 shows an exemplary display of the stroke information in agraphical user interface.

FIG. 3 shows a flow chart for a computer-implemented method forproviding a stroke information according to one or more exampleembodiments.

FIG. 4 shows a data processing system according to one or more exampleembodiments.

DETAILED DESCRIPTION

One or more example embodiments of the invention relates in one aspectto a computer-implemented method for providing a stroke information, themethod comprising:

-   -   Receiving examination data, the examination data comprising        computed tomography imaging data of an examination area of a        patient, the examination area of the patient comprising at least        one brain region, the at least one brain region being affected        by a stroke,    -   Adjusting a causal model based on the computed tomography        imaging data, thereby obtaining an adjusted causal model,        wherein the adjusted causal model models a first variable as a        first cause for an appearance of the examination area of the        patient,    -   Receiving a first value for the first variable,    -   Generating the stroke information based on the adjusted causal        model and the first value for the first variable,    -   Providing the stroke information.

Causal models allow to model causal relationships between multiplevariables. They can be typically visualized as graphs. They can not onlybe used to predict values for unobserved variables, but also to createso-called counterfactuals: what would the outcome be if a certainvariable was “retrospectively” set to a specific value? This is referredto as an intervention.

N. Pawlowski, D. Coelho de Castro, B. Glocker, Deep Structural CausalModels for Tractable Counterfactual Inference, Proceedings of NeurIPS2020, describe causal models that have been extended to tractablyinclude images among the variables in such a graph by using deepgenerative models that link the image space to a low-dimensionalembedding. Such a trained deep structural causal model can createcounterfactual images based on interventions regardingdemographic/clinical parameters. The variables are the image, age, sex,brain volume and ventricle volume. Counterfactual images can be createdby performing so-called interventions on the graph, such as virtuallychanging the sex of the patient.

The first variable may be a time information regarding an onset time ofthe stroke. The time information regarding the onset time of the strokemay be the time elapsed since the onset of the stroke.

Performing interventions regarding the onset time on such a causal modelwould allow users to inspect the model-predicted, patient-specificeffects of the stroke-induced ischemia over time. This would allowsimple, intuitive validity checks and insights such as: “Does turningback for at least the (known or estimated) onset time remove all signsof infarction in the image as it should?”, “Is the gradual growth of theinfarct core during given time periods projected to happen at areasonable pace?”, “Is the model's prediction for the degree ofdeterioration within the coming hour in line with my expectations?”.

The adjusted causal model may model a second variable as a second causefor the appearance of the examination area of the patient, wherein thesecond variable is selected from the group consisting of demographicinformation regarding the patient, non-imaging-based diagnosticinformation regarding the patient, medical image information,therapeutic information regarding a therapy of the stroke andcombinations thereof, wherein a first value for the second variable isreceived, wherein the stroke information is generated further based onthe first value for the second variable. The examination data mayfurther comprise an actual value for the first variable and/or an actualvalue for the second variable. The causal model may be adjusted furtherbased on the actual value for the first variable and/or on the actualvalue for the second variable.

Interventions may also be performed on other variables in the graph togenerate additional counterfactuals for reference, in addition tomodifying the temporal axis. Ideally, further variables could beconsidered jointly in form of the second variable, if available, toenhance the causal model's explanatory power. The demographicinformation, in particular general demographic information, regardingthe patient may comprise, for example, information regarding age, sexand/or further causes for image appearance. The non-imaging-baseddiagnostic information regarding the patient may comprise, for example,lab values, and/or NIHSS information.

The medical image information may comprise, for example, a non-contrastCT (NCCT) image, a CT angiography (CTA) image, a CT perfusion (CTP)image, a perfusion map and combinations thereof. The therapeuticinformation regarding a therapy of the stroke may comprise, for example,information regarding a therapeutic measure that is under consideration,for example, lysis and/or thrombectomy. Thus different treatment optionscan be rated with respect to their expected outcome based on therespective counterfactual images.

The adjusted causal model may model an image-derived information as aneffect of the first variable and/or second variable and as anintermediate cause for the appearance of the examination area of thepatient, wherein a value for the image-derived information is calculatedby applying the adjusted causal model onto the first value for the firstvariable and/or onto the first value of the second variable, wherein thestroke information is generated further based on the calculated valuefor the image-derived information, in particular, by applying theadjusted causal model onto the calculated value for the image-derivedinformation. An actual value for the image-derived information may becomprised in the examination data and/or may be derived from thecomputed tomography imaging data. The causal model may be adjustedfurther based on the actual value for the image-derived information.

The image-derived information may comprise, for example, an infarct coresize and/or a penumbra volume and/or a site of occlusion. The infarctcore size can be modeled, for example, as a cause for the appearance ofthe examination area and as an effect of the onset time. The site ofocclusion can be modeled, for example, as a cause for the appearance ofthe examination area as well as for the infarct core size. Similarly,the medical image information may be modeled, by the adjusted causalmodel, as an effect of the demographic information regarding the patientand/or of the non-imaging-based diagnostic information regarding thepatient

The adjusted causal model may model the first variable as the firstcause for the appearance of the examination area of the patientaccording to a first medical imaging technique. The adjusted causalmodel may model the appearance of the examination area of the patientaccording to the first medical imaging technique as a cause for theappearance of the examination area of the patient according to a secondmedical imaging technique. The first medical imaging technique may be,for example, a first computed tomography medical imaging technique. Thesecond medical imaging technique may be, for example, a second computedtomography medical imaging technique.

Different imaging techniques may be used, depending on the specificclinical case. Several of them may be included in a single causal model.

In one example, the first medical imaging technique is non-contrastcomputed tomography (NCCT) and the second medical imaging technique isCT angiography (CTA) and/or CT perfusion (CTP). In another example, thefirst medical imaging technique is CTA, and the second medical imagingtechnique is CTP. In another example, the first medical imagingtechnique is CTP, and the second medical imaging technique is aperfusion map. If the computed tomography imaging data comprise dualenergy and/or spectral acquisitions, any derived results, for exampleiodine maps and/or XMAP, may similarly be investigated along thetemporal domain given sufficient training data.

The first counterfactual medical image of the examination area of thepatient may be generated by applying the adjusted causal model onto thefirst value for the first variable and/or onto the first value for thesecond variable, wherein the stroke information comprises the firstcounterfactual medical image and/or is generated based on the firstcounterfactual medical image. The first counterfactual medical image mayrepresent the appearance of the examination area of the patient, inparticular, according to the first medical imaging technique and/oraccording to the second medical imaging technique. The secondcounterfactual medical image may represent the appearance of theexamination area of the patient, in particular, according to the firstmedical imaging technique and/or according to the second medical imagingtechnique.

To more thoroughly ensure that the produced counterfactual imagesclosely resemble the original scan, for example the actual medical imageof the examination area of the patient, also in non-pertinent imagecharacteristics (e.g., noise structure) and only differ in aspectsimmediately relevant to the (causal) intervention, strategies inspiredby those commonly used in (Cycle)GANs may be beneficial. In one example,an identity constraint may ensure that transforming to thelow-dimensional embedding and back preserves an image as accurately aspossible. In another example a fake-vs.-real discriminator networkand/or loss function operating in an image domain can be used toencourage the generator to produce highly realistic images. Anotheroption is to generate only difference/residual images, which serves toboth reduce the complexity of the output space the model needs toproduce as well as to preserve the original image characteristics asthese would be added on top of the residual image to obtain the finalresult.

A second value for the first variable and/or a second value for thesecond variable may be received, wherein a second counterfactual medicalimage of the examination area of the patient is generated by applyingthe adjusted causal model onto the second value for the first variableand/or onto the second value for the second variable, wherein the strokeinformation comprises the second counterfactual medical image and/or isgenerated further based on the second counterfactual medical image. Thesecond value for the first variable may be different from the firstvalue for the first variable. The second value for the second variablemay be different from the first value for the first variable.

A difference map may be calculated based on the first counterfactualmedical image and the second counterfactual medical image, wherein thestroke information comprises the difference map and/or is generatedfurther based on the difference map. The difference map may be, forexample, a density difference map. The stroke information may begenerated, for example, based on an overlay, in particular a color-codedoverlay, of the difference map on top of a reference image. Thereference image may be the second counterfactual medical image if thedifference map is calculated by subtracting the second counterfactualmedical image from the first counterfactual medical image. The referenceimage may be the first counterfactual medical image if the differencemap is calculated by subtracting the first counterfactual medical imagefrom the second counterfactual medical image.

Differences between time points may be visualized as color-codedoverlays on the scan to visualize temporal changes (e.g. infarct coregrowth) directly in a single image.

The examination data, in particular the computed tomography imagingdata, may comprise an actual medical image of the examination area ofthe patient, wherein an estimated value for the first variable isdetermined based on the first value for the first variable, the secondvalue for the first variable, the first counterfactual medical image,the second counterfactual medical image and the actual medical image,for example, by applying an interpolation and/or extrapolation, whereinthe stroke information comprises the estimated value for the firstvariable and/or is generated further based on the estimated value forthe first variable. The estimated value for the first variable may bedetermined, for example, as an estimate of an actual value for the firstvariable, wherein, according to the adjusted causal model, the actualvalue for the first variable causes the examination area of the patientto appear as in the actual medical image of the examination area of thepatient.

An estimated value for the second variable may be determined based onthe first value for the second variable, the second value for the secondvariable, the first counterfactual medical image, the secondcounterfactual medical image and the actual medical image, for example,by applying an interpolation and/or extrapolation, wherein the strokeinformation comprises the estimated value for the second variable and/oris generated further based on the estimated value for the secondvariable. The estimated value for the second variable may be determined,for example, as an estimate of an actual value for the second variable,wherein, according to the adjusted causal model, the actual value forthe second variable causes the examination area of the patient to appearas in the actual medical image of the examination area of the patient.

A difference map can be calculated based on the first counterfactualmedical image and the actual medical image and/or based on the secondcounterfactual medical image and the actual medical image, inparticular, as described above for the difference map calculated basedon the first counterfactual medical image and the second counterfactualmedical image.

When no (clinically or algorithmically) estimated onset time isavailable, the causal model itself can be used to probabilisticallyinfer a likely onset time from the given observations. To further refinesuch an estimate, counterfactual images could be created by performinginterventions for multiple time points in the vicinity of the estimateand compare them to the real scan—the generated image that exhibits theleast discrepancies could then be assumed to correspond to the mostaccurate onset time estimate.

The first value for the first variable may be a first time point afterthe onset of the stroke. The second value for the first variable may bea second time point after the onset of the stroke. The estimated valuefor the time information may be an estimate of the time elapsed betweenthe onset of the stroke and the acquisition of the computed tomographyimaging data of the examination area of the patient.

An additional option for automatic verification and quantification is torun algorithms that are able to classify healthy subjects vs. thosesuffering from stroke and/or rate the stroke severity on the generatedimages. Such algorithms could then track the results quantitatively in areproducible manner. For instance, an automatic ASPECT scoring algorithmmay run on the images generated for consecutive points in time mightcontinually decrease from 10 (no signs visible yet) to the final scoreas the signs in all affected regions are becoming increasinglyrecognizable. The results could be displayed alongside the images on atemporal axis to indicate the progression of stroke signs in terms ofstandardized clinical scores.

Presentation-wise, a “slider” element may be used to interactivelynavigate the temporal axis, thereby manually selecting the first timepoint after the onset of the stroke and/or the second time point afterthe onset of the stroke, with a live update of the images, and/or imagesmay be precomputed at certain time intervals and arranged for displayaccordingly.

The causal modal may be a deep structural causal model. The causal modelmay be based, in particular, trained, on a plurality of trainingdatasets, each training dataset of the plurality of training datasetscomprising computed tomography stroke imaging data and a respectivevalue for the first variable. Each training dataset of the plurality oftraining datasets may further comprise a respective value for the secondvariable and/or a respective value for the image-derived information.The plurality of training datasets may comprise training datasets from alarge number of patients. Based on the examination data of a patient,the causal model can be adjusted specifically to that patient.

One or more example embodiments of the invention relates to a dataprocessing system, comprising a data interface and a processor, the dataprocessing system being configured for carrying out a method accordingto one or more example embodiments of the invention.

One or more example embodiments of the invention relates to a computedtomography device comprising the data processing system. The computedtomography device may be configured for the acquisition of the computedtomography imaging data of the examination area of the patient.

One or more example embodiments of the invention relates to a computerprogram product or a computer-readable storage medium, comprisinginstructions which, when the instructions are executed by a computer,cause the computer to carry out the method according to one or moreexample embodiments of the invention.

Using a causal model as suggested ensures clinical meaningfulness incontrast to associative learning. The complete proposed approach makesallows proper incorporation of various stroke-relevant parameters.Having the possibility, given a brain scan as well as pertinent clinicalinformation, to virtually “turn back” or “advance” time since onset andobserve the estimated patient-specific effects directly in the imageswould appear to satisfy these needs. Based on the adjusted causal model,the clinical user may explore, how the patient's brain would have lookedlike or will it look like 0 h, 1 h, 2 h, . . . , 6 h after the onset ofthe stroke and check, whether this in line with the current observationand the corresponding (clinically determined or model-predicted) onsettime.

The method allows a flexible, learning-based onset time estimation andshort-term outcome prediction that not only incorporates both imagingand non-imaging-based information, but also uniquely offers intuitiveexploration and explanation of the results in the “language”radiologists know best—clinical images, synthetically generated for anytime point according to the model's interpretation of the case. Thecausal model combined with generative deep learning models can be usedto incorporate, among others, both the onset time as a temporal causalvariable as well as result variables representing the image spaceresults, allowing to synthetize images specifically tailored to thepatient at other time points which are not directly observable, and thusto foster a better case understanding in terms of precision medicine.This allows a more comprehensive assessment of stroke cases. Inparticular, the onset time and pre-generated images and/or overlayscould be displayed as part of the result portfolio.

Any of the algorithms and/or models mentioned herein can be based on oneor more of the following architectures: deep convolutional neuralnetwork, deep belief network, random forest, deep residual learning,deep reinforcement learning, recurrent neural network, Siamese network,generative adversarial network or auto-encoder.

The computer program product can be, for example, a computer program orcomprise another element apart from the computer program. This otherelement can be hardware, for example a memory device, on which thecomputer program is stored, a hardware key for using the computerprogram and the like, and/or software, for example, a documentation or asoftware key for using the computer program. A computer-readable storagemedium can be embodied as non-permanent main memory (e.g. random-accessmemory) or as permanent mass storage (e.g. hard disk, USB stick, SDcard, solid state disk).

The data processing system can comprise, for example, at least one of acloud-computing system, a distributed computing system, a computernetwork, a computer, a tablet computer, a smartphone or the like. Thedata processing system can comprise hardware and/or software. Thehardware can be, for example, a processor system, a memory system andcombinations thereof. The hardware can be configurable by the softwareand/or be operable by the software. Calculations for performing anaction of a method may be carried out in the processor.

Data, in particular each of the examination data, the first value of thefirst variable, the first value of the second variable, the second valueof the first variable and the second value of the second variable, canbe received, for example, by receiving a signal that carries the dataand/or by reading the data from a computer memory and/or by a manualuser input, for example, through a graphical user interface. Data, inparticular the stroke information, can be provided, for example, bytransmitting a signal that carries the data and/or by writing the datainto a computer memory and/or by displaying the data on a display.

In the context of the present invention, the expression “based on” canin particular be understood as meaning “using, inter alia”. Inparticular, wording according to which a first feature is calculated (orgenerated, determined etc.) based on a second feature does not precludethe possibility of the first feature being calculated (or generated,determined etc.) based on a third feature.

FIG. 1 shows an exemplary template for causal graphs representing anadjusted causal model. The first variable is a time information Nregarding an onset time of the stroke. The adjusted causal model modelsa second variable as a second cause for the appearance of theexamination area of the patient, wherein the second variable is selectedfrom the group consisting of demographic information 11 regarding thepatient, non-imaging-based diagnostic information 12 regarding thepatient, medical image information 3, therapeutic information 14regarding a therapy of the stroke and combinations thereof, wherein afirst value for the second variable is received, wherein the strokeinformation is generated further based on the first value for the secondvariable.

The adjusted causal model models an image-derived information 2 as aneffect of the first variable and as an intermediate cause for theappearance of the examination area of the patient, wherein a value forthe image-derived information 2 is calculated by applying the adjustedcausal model onto the first value for the first variable, wherein thestroke information is generated further based on the value for theimage-derived information 2.

In this example, the most essential interaction considered is that ofthe time information N regarding the onset time of the stroke on theappearance of the examination area of the patient according tonon-contrast computed tomography 31. Further (but not all) possiblecausal relationships are indicated with dashed arrows. They do notconstitute the only sensible way to view cause and effect. For instance,the image-derived information 2 might also be considered an effect ofthe medical image information 3. The depicted direction of causation canbe used to model the influence of the infarct core volume 21 (which ofcourse exists independently of whether it is quantified from the image)on the image appearance through interventions. Arrows from/to wholegroups (large boxes) signify that relationships between subsets of thevariables in both groups are meant without specifying the details ofsuch interactions, these are left to concrete applications.

The image-derived information 2 comprises an infarct core volume 21, apenumbra volume 22 and/or a site of occlusion 23. The medical imageinformation 3 comprises a non-contrast CT (NCCT) image 31, a CTangiography (CTA) image 32, a CT perfusion (CTP) image 33 and/or afollow-up NCCT image 34 (for example, 24 hours after the onset of thestroke and/or after treatment). The derived medical image information 4comprises dual-energy and/or spectral imaging results 41 and/orperfusion maps 42. The arrow 1A indicates that the demographicinformation 11 regarding the patient and/or the non-imaging-baseddiagnostic information 12 can be modeled, by the adjusted causal model,as a cause for the therapeutic information 14 regarding a therapy of thestroke.

FIG. 2 shows an exemplary display of the stroke information in agraphical user interface G. The examination area comprises the brain Hwith the left cerebral hemisphere HL and the right cerebral hemi-sphereHR. Counterfactual images for different points in time may be generatedinteractively by dragging the slider element TP with the pointer P tothe respective point on the time axis T. The arrow TA indicates thedirection towards increasing time.

The first value for the first variable is a first time point T1 after anonset of the stroke. The second value for the first variable is a secondtime point T2 after the onset of the stroke. The first counterfactualmedical image B1 of the examination area of the patient is generated byapplying the adjusted causal model onto the first value for the firstvariable in form of the first time point T1. The stroke informationcomprises the first counterfactual medical image B1 and is generatedbased on the first counterfactual medical image B1. A second value forthe first variable and/or a second value for the second variable isreceived, wherein a second counterfactual medical image B2 of theexamination area of the patient is generated by applying the adjustedcausal model onto the second value for the first variable in form of thesecond time point T2. The stroke information comprises the secondcounterfactual medical image B2 and is generated further based on thesecond counterfactual medical image B2.

The first counterfactual image B1 and the second counterfactual imageB2, one prior to and one later than the current scan, are shown. B1represents the estimated appearance at 4 hours after the onset of thestroke. B2 represents the predicted appearance at 8 hours after theonset of the stroke. BN represents the appearance of the examinationregion in the examination data. The estimated time TN that elapsedbetween the onset time of the stroke and the acquisition of theexamination data is 6 hours.

Similarly, such images could be arranged on a timeline fornon-interactive viewing. Using the image B2 as the baseline for adifference computation, as indicated by the checkbox C, the futureextent Y of the infarction X is displayed as an overlay in the otherimages.

FIG. 3 shows a flow chart for a computer-implemented method forproviding a stroke information, the method comprising:

-   -   Receiving S1 examination data, the examination data comprising        computed tomography imaging data of an examination area of a        patient, the examination area of the patient comprising at least        one brain region, the at least one brain region being affected        by a stroke,    -   Adjusting S2 a causal model based on the computed tomography        imaging data, thereby obtaining an adjusted causal model,        wherein the adjusted causal model models a first variable as a        first cause for an appearance of the examination area of the        patient,    -   Receiving S3 a first value for the first variable,    -   Generating S4 the stroke information based on the adjusted        causal model and the first value for the first variable,    -   Providing S5 the stroke information.

FIG. 4 shows a data processing system 8 comprising a data interface 8Aand a processor 8B, the data processing system 8 being configured forcarrying out a method as described with respect to FIG. 3 . For example,the processor 8B is configured to execute computer-readable instructionsto cause the data processing system 8 to perform the method of FIG. 3 .The processor 8B may store the computer-readable instructions on thedata processing system 8 may further include a memory from which theprocessor 8B retrieves the computer-readable instructions.

It will be understood that, although the terms first, second, etc. maybe used herein to describe various elements, components, regions,layers, and/or sections, these elements, components, regions, layers,and/or sections, should not be limited by these terms. These terms areonly used to distinguish one element from another. For example, a firstelement could be termed a second element, and, similarly, a secondelement could be termed a first element, without departing from thescope of example embodiments. As used herein, the term “and/or,”includes any and all combinations of one or more of the associatedlisted items. The phrase “at least one of” has the same meaning as“and/or”.

Spatially relative terms, such as “beneath,” “below,” “lower,” “under,”“above,” “upper,” and the like, may be used herein for ease ofdescription to describe one element or feature's relationship to anotherelement(s) or feature(s) as illustrated in the figures. It will beunderstood that the spatially relative terms are intended to encompassdifferent orientations of the device in use or operation in addition tothe orientation depicted in the figures. For example, if the device inthe figures is turned over, elements described as “below,” “beneath,” or“under,” other elements or features would then be oriented “above” theother elements or features. Thus, the example terms “below” and “under”may encompass both an orientation of above and below. The device may beotherwise oriented (rotated 90 degrees or at other orientations) and thespatially relative descriptors used herein interpreted accordingly. Inaddition, when an element is referred to as being “between” twoelements, the element may be the only element between the two elements,or one or more other intervening elements may be present.

Spatial and functional relationships between elements (for example,between modules) are described using various terms, including “on,”“connected,” “engaged,” “interfaced,” and “coupled.” Unless explicitlydescribed as being “direct,” when a relationship between first andsecond elements is described in the disclosure, that relationshipencompasses a direct relationship where no other intervening elementsare present between the first and second elements, and also an indirectrelationship where one or more intervening elements are present (eitherspatially or functionally) between the first and second elements. Incontrast, when an element is referred to as being “directly” on,connected, engaged, interfaced, or coupled to another element, there areno intervening elements present. Other words used to describe therelationship between elements should be interpreted in a like fashion(e.g., “between,” versus “directly between,” “adjacent,” versus“directly adjacent,” etc.).

The terminology used herein is for the purpose of describing particularembodiments only and is not intended to be limiting of exampleembodiments. As used herein, the terms “and/or” and “at least one of”include any and all combinations of one or more of the associated listeditems. It will be further understood that the terms “comprises,”“comprising,” “includes,” and/or “including,” when used herein, specifythe presence of stated features, integers, steps, operations, elements,and/or components, but do not preclude the presence or addition of oneor more other features, integers, steps, operations, elements,components, and/or groups thereof. As used herein, the term “and/or”includes any and all combinations of one or more of the associatedlisted items. Expressions such as “at least one of,” when preceding alist of elements, modify the entire list of elements and do not modifythe individual elements of the list. Also, the term “example” isintended to refer to an example or illustration.

It should also be noted that in some alternative implementations, thefunctions/acts noted may occur out of the order noted in the figures.For example, two figures shown in succession may in fact be executedsubstantially concurrently or may sometimes be executed in the reverseorder, depending upon the functionality/acts involved.

Unless otherwise defined, all terms (including technical and scientificterms) used herein have the same meaning as commonly understood by oneof ordinary skill in the art to which example embodiments belong. Itwill be further understood that terms, e.g., those defined in commonlyused dictionaries, should be interpreted as having a meaning that isconsistent with their meaning in the context of the relevant art andwill not be interpreted in an idealized or overly formal sense unlessexpressly so defined herein.

It is noted that some example embodiments may be described withreference to acts and symbolic representations of operations (e.g., inthe form of flow charts, flow diagrams, data flow diagrams, structurediagrams, block diagrams, etc.) that may be implemented in conjunctionwith units and/or devices discussed above. Although discussed in aparticularly manner, a function or operation specified in a specificblock may be performed differently from the flow specified in aflowchart, flow diagram, etc. For example, functions or operationsillustrated as being performed serially in two consecutive blocks mayactually be performed simultaneously, or in some cases be performed inreverse order. Although the flowcharts describe the operations assequential processes, many of the operations may be performed inparallel, concurrently or simultaneously. In addition, the order ofoperations may be re-arranged. The processes may be terminated whentheir operations are completed, but may also have additional steps notincluded in the figure. The processes may correspond to methods,functions, procedures, subroutines, subprograms, etc.

Specific structural and functional details disclosed herein are merelyrepresentative for purposes of describing example embodiments. Thepresent invention may, however, be embodied in many alternate forms andshould not be construed as limited to only the embodiments set forthherein.

In addition, or alternative, to that discussed above, units and/ordevices according to one or more example embodiments may be implementedusing hardware, software, and/or a combination thereof. For example,hardware devices may be implemented using processing circuitry such as,but not limited to, a processor, Central Processing Unit (CPU), acontroller, an arithmetic logic unit (ALU), a digital signal processor,a microcomputer, a field programmable gate array (FPGA), aSystem-on-Chip (SoC), a programmable logic unit, a microprocessor, orany other device capable of responding to and executing instructions ina defined manner. Portions of the example embodiments and correspondingdetailed description may be presented in terms of software, oralgorithms and symbolic representations of operation on data bits withina computer memory. These descriptions and representations are the onesby which those of ordinary skill in the art effectively convey thesubstance of their work to others of ordinary skill in the art. Analgorithm, as the term is used here, and as it is used generally, isconceived to be a self-consistent sequence of steps leading to a desiredresult. The steps are those requiring physical manipulations of physicalquantities. Usually, though not necessarily, these quantities take theform of optical, electrical, or magnetic signals capable of beingstored, transferred, combined, compared, and otherwise manipulated. Ithas proven convenient at times, principally for reasons of common usage,to refer to these signals as bits, values, elements, symbols,characters, terms, numbers, or the like.

It should be borne in mind that all of these and similar terms are to beassociated with the appropriate physical quantities and are merelyconvenient labels applied to these quantities. Unless specificallystated otherwise, or as is apparent from the discussion, terms such as“processing” or “computing” or “calculating” or “determining” of“displaying” or the like, refer to the action and processes of acomputer system, or similar electronic computing device/hardware, thatmanipulates and transforms data represented as physical, electronicquantities within the computer system's registers and memories intoother data similarly represented as physical quantities within thecomputer system memories or registers or other such information storage,transmission or display devices.

The interface circuits may include wired or wireless interfaces that areconnected to a local area network (LAN), the Internet, a wide areanetwork (WAN), or combinations thereof. The functionality of any givenmodule of the present disclosure may be distributed among multiplemodules that are connected via interface circuits. For example, multiplemodules may allow load balancing. In a further example, a server (alsoknown as remote, or cloud) module may accomplish some functionality onbehalf of a client module.

When a hardware device is a computer processing device (e.g., aprocessor, Central Processing Unit (CPU), a controller, an arithmeticlogic unit (ALU), a digital signal processor, a microcomputer, amicroprocessor, etc.), the computer processing device may be configuredto carry out program code by performing arithmetical, logical, andinput/output operations, according to the program code. Once the programcode is loaded into a computer processing device, the computerprocessing device may be programmed to perform the program code, therebytransforming the computer processing device into a special purposecomputer processing device. In a more specific example, when the programcode is loaded into a processor, the processor becomes programmed toperform the program code and operations corresponding thereto, therebytransforming the processor into a special purpose processor.

Software and/or data may be embodied permanently or temporarily in anytype of machine, component, physical or virtual equipment, or computerstorage medium or device, capable of providing instructions or data to,or being interpreted by, a hardware device. The software also may bedistributed over network coupled computer systems so that the softwareis stored and executed in a distributed fashion. In particular, forexample, software and data may be stored by one or more computerreadable recording mediums, including the tangible or non-transitorycomputer-readable storage media discussed herein.

Even further, any of the disclosed methods may be embodied in the formof a program or software. The program or software may be stored on anon-transitory computer readable medium and is adapted to perform anyone of the aforementioned methods when run on a computer device (adevice including a processor). Thus, the non-transitory, tangiblecomputer readable medium, is adapted to store information and is adaptedto interact with a data processing system or computer device to executethe program of any of the above mentioned embodiments and/or to performthe method of any of the above mentioned embodiments.

Example embodiments may be described with reference to acts and symbolicrepresentations of operations (e.g., in the form of flow charts, flowdiagrams, data flow diagrams, structure diagrams, block diagrams, etc.)that may be implemented in conjunction with units and/or devicesdiscussed in more detail below. Although discussed in a particularlymanner, a function or operation specified in a specific block may beperformed differently from the flow specified in a flowchart, flowdiagram, etc. For example, functions or operations illustrated as beingperformed serially in two consecutive blocks may actually be performedsimultaneously, or in some cases be performed in reverse order.

Wherever meaningful, individual embodiments or their individual aspectsand features can be combined or exchanged with one another withoutlimiting or widening the scope of the present invention. Advantageswhich are described with respect to one embodiment of the presentinvention are, wherever applicable, also advantageous to otherembodiments of the present invention.

1. A computer-implemented method for providing a stroke information, themethod comprising: receiving examination data, the examination datacomprising computed tomography imaging data of an examination area of apatient, the examination area of the patient comprising at least onebrain region, the at least one brain region being affected by a stroke;adjusting a causal model based on the computed tomography imaging datato obtain an adjusted causal model, wherein the adjusted causal modelmodels a first variable as a first cause for an appearance of theexamination area of the patient; receiving a first value for the firstvariable; generating the stroke information based on the adjusted causalmodel and the first value for the first variable; and providing thestroke information.
 2. The method of claim 1, wherein the first variableis a time information regarding an onset time of the stroke.
 3. Themethod of claim 1, wherein the adjusted causal model models a secondvariable as a second cause for the appearance of the examination area ofthe patient, the second variable includes demographic informationregarding the patient, non-imaging-based diagnostic informationregarding the patient, medical image information, therapeuticinformation regarding a therapy of the stroke, a subcombination thereofor a combination thereof, the receiving receives a first value for thesecond variable, and the generating generates the stroke informationfurther based on the first value for the second variable.
 4. The methodof claim 1, wherein the adjusted causal model models an image-derivedinformation as an effect of the first variable and as an intermediatecause for the appearance of the examination area of the patient and themethod further comprises: calculating a value for the image-derived byapplying the adjusted causal model onto the first value for the firstvariable, wherein the generating the stroke information generates thestroke information further based on the value for the image-derivedinformation.
 5. The method of claim 1, wherein the adjusted causal modelmodels the first variable as the first cause for the appearance of theexamination area of the patient according to a first medical imagingtechnique, and the adjusted causal model models the appearance of theexamination area of the patient according to the first medical imagingtechnique as a cause for the appearance of the examination area of thepatient according to a second medical imaging technique.
 6. The methodof claim 3, further comprising: generating a first counterfactualmedical image of the examination area of the patient by applying theadjusted causal model onto the first value for the first variable,wherein at least one of, the stroke information includes the firstcounterfactual medical image or the generating the stroke informationgenerates the stroke information based on the first counterfactualmedical image.
 7. The method of claim 6, wherein the receiving receivesat least one of a second value for the first variable or a second valuefor the second variable and the method further comprises: generating asecond counterfactual medical image of the examination area of thepatient by applying the adjusted causal model onto at least one of thesecond value for the first variable or the second value for the secondvariable, wherein at least one of, the stroke information includes thesecond counterfactual medical image, or the generating the strokeinformation generates the stroke information further based on the secondcounterfactual medical image.
 8. The method of claim 7, furthercomprising: calculating a difference map based on the firstcounterfactual medical image and the second counterfactual medicalimage, wherein at least one of, the stroke information includes thedifference map, or the generating the stroke information generates thestroke information further based on the difference map.
 9. The method ofclaim 7, wherein the examination data comprise an actual medical imageof the examination area of the patient and the method further comprises:determining an estimated value for the first variable based on the firstvalue for the first variable, the second value for the first variable,the first counterfactual medical image, the second counterfactualmedical image and the actual medical image, wherein at least one of, thestroke information includes the estimated value for the first variable,or the generating the stroke information generates the strokeinformation further based on the estimated value for the first variable.10. The method of claim 7, wherein at least one of, the first value forthe first variable is a first time point after an onset of the stroke,or the second value for the first variable is a second time point afterthe onset of the stroke.
 11. The method of claim 1, wherein the causalmodal is a deep structural causal model.
 12. The method of claim 1,wherein the causal model is based on a plurality of training datasets,each training dataset of the plurality of training datasets comprisingcomputed tomography stroke imaging data and a respective value for thefirst variable.
 13. A data processing system, comprising: a datainterface; and a processor, the data processing system being configuredto perform the method of claim
 1. 14. A computed tomography devicecomprising: the data processing system of claim 13, wherein the computedtomography device is configured to acquire the computed tomographyimaging data of the examination area of the patient.
 15. Anon-transitory computer-readable storage medium, comprising instructionsincluding instructions which, when executed by a computer, cause thecomputer to perform the method of claim
 1. 16. The method of claim 3,wherein the adjusted causal model models an image-derived information asan effect of the first variable and as an intermediate cause for theappearance of the examination area of the patient and the method furthercomprises: calculating a value for the image-derived by applying theadjusted causal model onto the first value for the first variable,wherein the generating the stroke information generates the strokeinformation further based on the value for the image-derivedinformation.
 17. The method of claim 16, wherein the adjusted causalmodel models the first variable as the first cause for the appearance ofthe examination area of the patient according to a first medical imagingtechnique, and the adjusted causal model models the appearance of theexamination area of the patient according to the first medical imagingtechnique as a cause for the appearance of the examination area of thepatient according to a second medical imaging technique.
 18. The methodof claim 17, further comprising: generating a first counterfactualmedical image of the examination area of the patient by applying theadjusted causal model onto the first value for the first variable,wherein at least one of, the stroke information includes the firstcounterfactual medical image or the generating the stroke informationgenerates the stroke information based on the first counterfactualmedical image.
 19. The method of claim 18, wherein the receivingreceives at least one of a second value for the first variable or asecond value for the second variable and the method further comprises:generating a second counterfactual medical image of the examination areaof the patient by applying the adjusted causal model onto at least oneof the second value for the first variable or the second value for thesecond variable, wherein at least one of, the stroke informationincludes the second counterfactual medical image, or the generating thestroke information generates the stroke information further based on thesecond counterfactual medical image.
 20. The method of claim 19, whereinat least one of, the first value for the first variable is a first timepoint after an onset of the stroke, or the second value for the firstvariable is a second time point after the onset of the stroke.